scholarly journals Population-Based Optimization Algorithms for Solving the Travelling Salesman Problem

10.5772/5586 ◽  
2008 ◽  
Author(s):  
Mohammad Reza ◽  
Mostafa Rahimi ◽  
Hamed Shah-Hosseini
Keyword(s):  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Population Based ◽  
Travelling Salesman

scholarly journals Power Efficient Hierarchical Scheduling for DSP Transformations

VLSI Design ◽  
2002 ◽  
Vol 14 (2) ◽  
pp. 203-217
Author(s):  
P. K. Merakos ◽  
K. Masselos ◽  
C. E. Goutis
Keyword(s):  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Digital Signal ◽  
Scheduling Problem ◽  
Switching Activity ◽  
Travelling Salesman ◽  
Hierarchical Scheduling ◽  
Power Efficient ◽  
Target Architecture ◽  
Selection For

In this paper, the problem of scheduling the computation of partial products in transformational Digital Signal Processing (DSP) algorithms, aiming at the minimization of the switching activity in data and address buses, is addressed. The problem is stated as a hierarchical scheduling problem. Two different optimization algorithms, which are based on the Travelling Salesman Problem (TSP), are defined. The proposed optimization algorithms are independent on the target architecture and can be adapted to take into account it. Experimental results obtained from the application of the proposed algorithms in various widely used DSP transformations, like Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT), show that significant switching activity savings in data and address buses can be achieved, resulting in corresponding power savings. In addition, the differences between the two proposed methods are underlined, providing envisage for their suitable selection for implementation, in particular transformational algorithms and architectures.

scholarly journals Comparative Study on Travelling Salesman Problem

2021 ◽  
Vol 23 (07) ◽  
pp. 853-857
Author(s):  
Yatharth Srivastav ◽  
◽  
J.K. Saini ◽  
Keyword(s):  
Comparative Study ◽  
Travelling Salesman Problem ◽  
Optimization Algorithms ◽  
Minimum Cost ◽  
Optimization Techniques ◽  
Travelling Salesman

Travelling Salesman Problem (TSP) is a kind of LPP to find a minimum cost sequence in order to travel in each set of cities in a way that starting as well as ending should be on the same city and each city is visited exactly one time. In this paper, we will compare different optimization algorithms working principles, and we will also discuss the advantages and limitations of all the optimization techniques.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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